def test_points_selection_01(self):
number_of_samples = 5
x = np.array([[8.79], [0.35], [4.56]])
data_feed = LatinHypercubeSampling(self.test_data, number_of_samples)
output = data_feed.points_selection(self.test_data.values, x)
expected_output = np.array([[9., 100.], [0., 1.], [5., 36.]])
self.assertEqual(output.tolist(), expected_output.tolist())
def test_lhs_points_generation_01(self):
number_of_samples = 1
data_feed = LatinHypercubeSampling(
self.test_data, number_of_samples) # Three samples requested.
output_1 = data_feed.lhs_points_generation()
# Test set 1: Check that the output has the right shape
self.assertEqual(output_1.shape[0], number_of_samples)
self.assertEqual(output_1.shape[1], (self.test_data.shape[1] - 1))
# Test set 2: Check that the values returned are within the expected range
expected_lower = 0 # Minimum value for x in input_array
expected_upper = 1 # Maximum value for x in input_array
self.assertTrue((output_1 > expected_lower)
and (output_1 < expected_upper))
def test_initialization_05(self):
output_2 = LatinHypercubeSampling(self.test_data)
expected_output = 5
expected_output_2 = np.array([[0], [1], [2], [3], [4], [5], [6], [7],
[8], [9]])
self.assertEqual(output_2.number_of_samples, expected_output)
self.assertEqual(output_2.x_data.tolist(), expected_output_2.tolist())
def test_lhs_points_generation_03(self):
number_of_samples = 10
data_feed = LatinHypercubeSampling(
self.test_data, number_of_samples) # Three samples requested.
output_1 = data_feed.lhs_points_generation()
# Test set 1: Check that the output has the right shape
self.assertEqual(output_1.shape[0], number_of_samples)
self.assertEqual(output_1.shape[1], (self.test_data.shape[1] - 1))
# Test set 2: Check that the values returned are within the expected range.
expected_lower_firstvalue = 0
expected_upper_lastvalue = 1
expected_upper_firstvalue = expected_upper_lastvalue * 1 / number_of_samples
expected_lower_lastvalue = expected_upper_lastvalue * (
number_of_samples - 1) / number_of_samples
self.assertTrue((output_1[0, 0] > expected_lower_firstvalue)
and (output_1[0, 0] < expected_upper_firstvalue))
self.assertTrue(
(output_1[number_of_samples - 1, 0] > expected_lower_lastvalue) and
(output_1[number_of_samples - 1, 0] < expected_upper_lastvalue))
def test_random_shuffling(self):
x_pure = np.array([[0, 1, 2, 3], [3, 4, 5, 6], [6, 7, 8, 9],
[9, 10, 11, 12], [12, 13, 14, 15], [15, 16, 17,
18]])
x = np.array([[0, 1, 2, 3], [3, 4, 5, 6], [6, 7, 8, 9],
[9, 10, 11, 12], [12, 13, 14, 15], [15, 16, 17, 18]])
output_1 = LatinHypercubeSampling.random_shuffling(x)
# Test 1: Check that randomization of columns has been done by comparing individual columns before and after
self.assertNotEqual(output_1[:, 0].tolist(), x_pure[:, 0].tolist())
self.assertNotEqual(output_1[:, 1].tolist(), x_pure[:, 1].tolist())
self.assertNotEqual(output_1[:, 2].tolist(), x_pure[:, 2].tolist())
self.assertNotEqual(output_1[:, 3].tolist(), x_pure[:, 3].tolist())
# Test 2: Check that the columns still contain exactly the same values as before randomization by comparing the sets
self.assertEqual(set(output_1[:, 0]), set(x_pure[:, 0]))
self.assertEqual(set(output_1[:, 1]), set(x_pure[:, 1]))
self.assertEqual(set(output_1[:, 2]), set(x_pure[:, 2]))
self.assertEqual(set(output_1[:, 3]), set(x_pure[:, 3]))
def test_initialization_08(self):
test_data_as_list = self.test_data.values.tolist()
with self.assertRaises(ValueError):
LatinHypercubeSampling(test_data_as_list, 7)
def test_initialization_07(self):
test_data_as_array = self.test_data.values
output_1 = LatinHypercubeSampling(test_data_as_array, 7)
np.testing.assert_array_equal(output_1.data, test_data_as_array)
def test_initialization_06(self):
with self.assertRaises(Exception):
LatinHypercubeSampling(self.test_data, 1.7)